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Building an effective operating model for enterprise analytics

For organizations that successfully derive value from predictive analytics, decision management and business process management are part of their operating models. This is where many companies leave vast potential untapped, often due to undefined scope and structure of the task.

Developing an operating model that delivers value is no small task. Investigate which parts of the organization can embrace predictive analytics and related concepts such as error rates, costs associated with errors, investment costs and ROI.

While making the investment in a predictive analytics infrastructure requires significant resources, acquiring the technology is only the beginning. Processes must be built and refined to fit diverse parts of the business; a process that fits sales force workflow may not work for finance teams.

The best performers in this field know how to translate a predictive analytics solution into how their business operates. These leaders go beyond analyzing data to build broad capacities for business intelligence, including:

  • Creating a supportive culture within their companies and building the leadership teams required to exploit the most from predictive analytics
  • Garnering enthusiasm and commitment at all levels of the organization to incorporate predictive analytics into strategic priorities of the business
  • Developing a context for defining business value, capturing appropriate data and relating it to other data sources
  • Devising processes to share and incorporate findings within diverse areas of the organization
  • Clearly defining the operating model needed to become an analytical competitor, incl the talent, processes, structure and technology

The major components of an analytics operating model are Information Management, Data Governance, Executive Leadership, Talent Management, Insight Development, Insight Integration, Technology Architecture, Value Measurement.

Utilizing analytics to drive daily decision-making can influence value levers across operations. Consider which areas of your organization can gain benefit from this approach, such as:

  • Financial management: Increasing revenues and operating income and reducing capital expenditures and operational expenses
  • Risk management: Calculating risks of prospective partnerships or marketing efforts and measuring organizational risk
  • Customer relationship management: Improving customer satisfaction and retention, increasing cross- and up-sells and utilizing loyalty programs
  • Workforce management: Improving promotion effectiveness, streamlining staff costs and reducing support costs
  • Product development and marketing: Designing customized products, targeting new markets and optimizing pricing

Before you dive into predictive analytics, assess the resources available within your organization. Review and confirm your data sources and budget, as well as technology and workforce requirements. Each of these factors has intrinsic impact upon your potential to develop and make effective use of predictive analytics.

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